Exploring fear in urban environments: Place and space analysis of social media data.
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| Title: | Exploring fear in urban environments: Place and space analysis of social media data. |
|---|---|
| Authors: | Zhou, Ying1 (AUTHOR) yzhou59@buffalo.edu, Crooks, Andrew1 (AUTHOR) atcrooks@buffalo.edu |
| Source: | Applied Geography. Jul2026, Vol. 192, pN.PAG-N.PAG. 1p. |
| Subjects: | Fear of crime, Geographic spatial analysis, Content analysis, Public safety, Natural language processing, Emotions, Urban planning |
| Geographic Terms: | New York (N.Y.) |
| Abstract: | One goal of creating livable cities is to enhance public safety. While previous research in urban studies has focused on correlations between physical environments and crime, it has typically relied on criminal statistics. However, fear of crime is an emotional response to perceived risks rather than a direct reflection of crime levels, so it cannot be analyzed solely by crime data. Additionally, urban planning today has gradually shifted its focus from a top-down to a bottom-up approach, making it essential to understand and foster spaces where residents feel safe. This research examines the spaces and places where people experience fear, as well as the factors that contribute to it, in New York City. We utilized social media data to gather people's expressions of the city and identified posts expressing fear emotion using the RoBERTa-based model and a rule-based classifier. Then, the selected social media data and crime were compared temporally by weekly trends and spatially by clustering methods (i.e., Hotspot Analysis (Getis-Ord Gi∗) and Local Moran's I). The results show that their temporal and spatial patterns partially have limited alignment. To delve into the origins of fear, we extend our analysis by adopting BERTopic to identify topics and summarize them into themes (e.g., places, transportation, people, others) to understand the bottom-up emergence of fear, thereby informing a people-centered approach to research on urban issues. • Natural Language Processing methods for detecting emotions from social media data. • The relationship between safety-related fear and crime. • The factors of place and space that contribute to fear. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Geography is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194296720 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Exploring fear in urban environments: Place and space analysis of social media data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Ying%22">Zhou, Ying</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yzhou59@buffalo.edu</i><br /><searchLink fieldCode="AR" term="%22Crooks%2C+Andrew%22">Crooks, Andrew</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> atcrooks@buffalo.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Geography%22">Applied Geography</searchLink>. Jul2026, Vol. 192, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Fear+of+crime%22">Fear of crime</searchLink><br /><searchLink fieldCode="DE" term="%22Geographic+spatial+analysis%22">Geographic spatial analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Content+analysis%22">Content analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Public+safety%22">Public safety</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Emotions%22">Emotions</searchLink><br /><searchLink fieldCode="DE" term="%22Urban+planning%22">Urban planning</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22New+York+%28N%2EY%2E%29%22">New York (N.Y.)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: One goal of creating livable cities is to enhance public safety. While previous research in urban studies has focused on correlations between physical environments and crime, it has typically relied on criminal statistics. However, fear of crime is an emotional response to perceived risks rather than a direct reflection of crime levels, so it cannot be analyzed solely by crime data. Additionally, urban planning today has gradually shifted its focus from a top-down to a bottom-up approach, making it essential to understand and foster spaces where residents feel safe. This research examines the spaces and places where people experience fear, as well as the factors that contribute to it, in New York City. We utilized social media data to gather people's expressions of the city and identified posts expressing fear emotion using the RoBERTa-based model and a rule-based classifier. Then, the selected social media data and crime were compared temporally by weekly trends and spatially by clustering methods (i.e., Hotspot Analysis (Getis-Ord Gi∗) and Local Moran's I). The results show that their temporal and spatial patterns partially have limited alignment. To delve into the origins of fear, we extend our analysis by adopting BERTopic to identify topics and summarize them into themes (e.g., places, transportation, people, others) to understand the bottom-up emergence of fear, thereby informing a people-centered approach to research on urban issues. • Natural Language Processing methods for detecting emotions from social media data. • The relationship between safety-related fear and crime. • The factors of place and space that contribute to fear. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Geography is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.apgeog.2026.104051 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Fear of crime Type: general – SubjectFull: Geographic spatial analysis Type: general – SubjectFull: Content analysis Type: general – SubjectFull: Public safety Type: general – SubjectFull: Natural language processing Type: general – SubjectFull: Emotions Type: general – SubjectFull: Urban planning Type: general – SubjectFull: New York (N.Y.) Type: general Titles: – TitleFull: Exploring fear in urban environments: Place and space analysis of social media data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhou, Ying – PersonEntity: Name: NameFull: Crooks, Andrew IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01436228 Numbering: – Type: volume Value: 192 Titles: – TitleFull: Applied Geography Type: main |
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